In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.

Information security is a crucial factor when communicating sensitive information between two parties. Steganography is one of the most techniques used for this purpose. This paper aims to enhance the capacity and robustness of hiding information by compressing image data to a small size while maintaining high quality so that the secret information remains invisible and only the sender and recipient can recognize the transmission. Three techniques are employed to conceal color and gray images, the Wavelet Color Process Technique (WCPT), Wavelet Gray Process Technique (WGPT), and Hybrid Gray Process Technique (HGPT). A comparison between the first and second techniques according to quality metrics, Root-Mean-Square Error (RMSE), Compression-

Recently, Image enhancement techniques can be represented as one of the most significant topics in the field of digital image processing. The basic problem in the enhancement method is how to remove noise or improve digital image details. In the current research a method for digital image de-noising and its detail sharpening/highlighted was proposed. The proposed approach uses fuzzy logic technique to process each pixel inside entire image, and then take the decision if it is noisy or need more processing for highlighting. This issue is performed by examining the degree of association with neighboring elements based on fuzzy algorithm. The proposed de-noising approach was evaluated by some standard images after corrupting them with impulse

In this paper, an algorithm through which we can embed more data than the
regular methods under spatial domain is introduced. We compressed the secret data
using Huffman coding and then this compressed data is embedded using laplacian
sharpening method.
We used Laplace filters to determine the effective hiding places, then based on
threshold value we found the places with the highest values acquired from these filters
for embedding the watermark. In this work our aim is increasing the capacity of
information which is to be embedded by using Huffman code and at the same time
increasing the security of the algorithm by hiding data in the places that have highest
values of edges and less noticeable.
The perform

Background: Obesity is a worldwide challenge and is closely
connected to many metabolic diseases. Two types of
adipose tissue, white adipose tissue (WAT) and brown
adipose tissue (BAT) have been identified. White fat cells
store chemical energy, brown adipocytes defend against
hypothermia, obesity and diabetes.
Objective: To localize and quantify brown adipocytes in
human subcutaneous (S) and visceral (V) adipose tissue by
histology and immunohistochemistry.
Type of the study: A cross –sectional study.
Methods: Adipose tissue was obtained from histopathology
specimens taken from ten patients, of different age, sex and
body mass index (BMI), undergoing surgery for different
pathologies

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co